This has two implications. First, parents use the same income-dependent relative preferences to measure their children’s well-being. Second, since parents obtain their own utility from consumption in adulthood, they care about their children’s consumption in their adulthood. To capture this latter aspect, I assume that parents are concerned with children’s growth that signals the children’s consumption in adulthood and measure children’s growth with educational output.
This is rooted in the findings in Kageyama
（2012） , which empirically showed that the relationships between LEGAP and these happiness indicators are bidirectional. In one direction, LEGAP negatively affects both HPN and HPGAP . An increase in LEGAP raises women ’ s widowhood ratio, and, since widows are, on average, less happy, it lowers women ’ s average happiness, HPN, and HPGAP . We call this effect the “ marital-status composition effect ” as the marital-status composition plays a central role.
in X j . There are dummies indicating whether a fund charges a
load, and if it is a rear or deferred load. Loads are a pricing element (which we have already amortized into the price mea- sure), but they also indicate funds sold with bundled broker services that investors may value. Rear or deferred loads indicate the presence of formal switching costs to removing assets from the fund. We also include a dummy if the fund is an exchange- traded fund (i.e., SPDRs or Barclay’s iShares) to control for the special liquidity and intraday pricing features of ETFs. We mea- sure the number of additional share classes attached to the fund’s portfolio; for a single-share-class fund this value is zero. The number of other funds managed by the same management com- pany is included to capture any value from being associated with a large fund family. Fund age is in the regressions as well. (Here, both the number of family funds and age enter in logs to parsimoniously embody diminishing marginal effects. Recall that we instrument for age because of its possible correlation with unobservable quality.) We add the current fund manag- er’s tenure, measured in years, as a covariate. And while all of the funds in our sample seek to match the return profile of the S&P 500 index, they do exhibit some small differences in their financial characteristics. These can result from skilled trading activities by a fund’s management despite having a severely constrained portfolio. We thus include measures of tax expo- sure (the taxable distributions yield rate), the yearly average of the ratio of monthly fund returns to those of the S&P 500 index, and the standard deviation of monthly returns. To the extent that fund buyers prefer any persistent positive varia- tions in financial performance, these controls should capture much of this effect. 32
Acquirers is the percentage of close CRE potential acquirers whose Tier 1 capital ratios is above the median Tier 1 capital ratio across local potential acquirers. P50 Tier 1 Capital Ratio of HHI Potential Acquirers is the median Tier 1 capital ratio of all potential acquirers whose acquisition of the failed bank would increase local deposit market concentration. % Well-Capitalized HHI Potential Acquirers is the percentage of potential acquirers whose Tier 1 capital ratio is above the median Tier 1 capital ratio among the group of potential acquirers whose acquisition of the failed bank would increase local deposit market concentration. Potential acquirer controls include Size, Liquidity Ratio, % Residential Loans, % CRE Loans, % C&I Loans, % Consumer Loans, 30-89PD Ratio, NPL Ratio, OREO Ratio, and Unused Commitment Ratio . Specifications (3) to (6) include failed bank fixed effects and potential acquirer-quarter fixed effects. Standard errors are presented in parentheses, and are clustered at the level of the failed bank’s state headquarters. ***, **, and * represent statistical significance at 1%, 5%, and 10% levels, respectively.
The audit market’s unique combination of features—its role in capital mar- ket transparency, mandated demand, and concentrated supply—means it re- ceives considerable attention from policy makers. We explore the effects of two market scenarios that have been the focus of policy discussions: manda- tory audit firm rotation and further supply concentration due to the exit of a “Big 4” audit firm. To do so, we first estimate publicly traded firms’ demand for auditing services, allowing the services provided by each of the Big 4 to be differentiated products. We then use those estimates to calculate how each scenario would affect client firms’ consumer surplus. We estimate that, for U.S. publicly trade firms, mandatory audit firm rotation would induce con- sumer surplus losses of approximately $2.7 billion if rotation were required after 10 years and $4.7–5.0 billion if after only four years. We find similarly that exit by one of the Big 4 would reduce client firms’ surplus by $1.4–1.8 billion. These estimates reflect only the value of firms’ lost options to hire the exiting audit firm; they do not include likely fee increases resulting from less
al. (2014) also show that optimal bid shading in these auctions also distorts the efficiency of the allocations, and thus a general ranking of expected revenues from discriminatory and uniform price auctions can not be made without knowledge about the specific features of bidder demand.
Given the theoretical vacuum, a variety of empirical approaches have been employed to as- sess the efficacy of Treasury auction mechanisms. The Treasury’s own study of this question, as reported by Malvey and Archibald (1998), was based on experimentation with the uniform price format for 2- and 5-year notes. To assess the revenue properties of the uniform vs. the status-quo discriminatory format, Malvey and Archibald calculated the auction-when-issued rate spread, and did not statistically reject a mean difference across the different auction formats. However, they note that the uniform price auctions “produce a broader distribution of auction awards” across bidders, and especially a lowered concentration of awards to top primary dealers.
returns would be difficult. 13
Table 3 describes our benchmark sample for estimating our firm investment model. We focus on manufacturing firms. Our main sample period runs from 1997 to 2000, although we also use data from 1995–1996 to compute the pre-sample period’s loan shares. We first drop observations missing investment rates or Basel I capital adequacy ratios. We then drop observations with a machine investment rate (the ratio of machine investment to machine capital stock) greater than 2 or less than −2. We further drop the observations of firms that owe more than 20% of total outstanding long-term loans to banks missing BCR data from Nikkei NEEDS during 1997–2000 . We also drop the observations of firms that borrowed mainly from the LTCB, NCB, insurance companies, and government financial institutions, because the LTCB and NCB were nationalized in 1998 and insurance companies and government financial institutions are not under bank regulations. Finally, we drop observations missing values for explanatory variables, which leads to a final sample of 2552 observations.